In our paper we demonstrate the use of popular RL algorithms in playing the Royal Game of Ur. The use of RL algorithms to solve popular board games is not new [1]} [2]} [3]} [4]} [5]}, but the use of RL for solving Ur has not been attempted yet. The Game of Ur is known to be a fairly complex, two-player board game, with variable number of states and pawns. A complete background on Ur is given in section REF . We have compared the performance of on-policy Monte Carlo, Q-learning and Expected Sarsa on playing The Game of Ur, in an independent multi-agent setting. We compare these RL algorithms’ application to Ur, implemented in a simulator, similar to Open AI’s Gym [6]}. For the implementation, we create our own simulator from scratch (see section REF , with similar functions as implemented in [6]}. Our goal is to test the performance of these algorithms on the simulator, and for the agents to be able to achieve human level strategies. The algorithm is not provided any game-specific information or hand-designed features, and are not privy to the internal state of the simulator. Through the simulator, the algorithms are just given the state space and the possible actions to take, as well as the information that they already have from their previous actions.
